CPMAI AI Project Exam Prep practice questions and answers 2026. Tap an option to test yourself — you'll see the correct answer and a plain-English explanation for every question. Free, no login.
Q11What is the primary advantage of using a probabilistic approach over a deterministic approach in machine learning?
✓ Correct answer: B. It provides a measure of uncertainty in predictions
Probabilistic approaches in machine learning provide a measure of uncertainty or confidence in predictions, which is crucial for risk assessment and decision-making, especially in applications where understanding prediction confidence is important.
Q12Which feature engineering technique is most appropriate when dealing with categorical variables that have a large number of possible values?
✓ Correct answer: A. Target encoding
One-hot encoding creates binary features for each category, but with many categories, this can lead to excessive dimensionality. Target encoding replaces categories with their mean target value, effectively handling high-cardinality categorical variables without creating too many new features.
Q13A company is implementing a machine learning model to predict equipment failures in a manufacturing plant. Which type of machine learning problem is this?
✓ Correct answer: D. Binary classification
Predicting equipment failures is a binary classification problem where the model predicts whether a failure will occur (positive class) or not (negative class) based on historical data of equipment conditions and past failures.
Q14What is the primary purpose of vectorization in natural language processing?
✓ Correct answer: C. Converting text data into numerical representations that algorithms can process
Vectorization in NLP converts text data into numerical representations (vectors) that machine learning algorithms can process. These vector representations capture semantic relationships between words or documents, enabling algorithms to work with text data.
Q15Which of the following is a key characteristic of Large Language Models (LLMs) like GPT?
✓ Correct answer: B. They use transformer architectures with self-attention mechanisms
Large Language Models like GPT use transformer architectures with self-attention mechanisms that allow them to consider the context of words in relation to all other words in a sequence, leading to better understanding of language context and meaning.
Q16In the context of machine learning, what is the primary difference between prediction and inference?
✓ Correct answer: A. Prediction focuses on forecasting specific outcomes, while inference aims to understand underlying patterns and relationships
Prediction focuses on using a trained model to make specific forecasts or classifications on new data, while inference involves drawing broader conclusions or insights about patterns, relationships, and underlying mechanisms in the data.
Q17What is a key advantage of generative AI compared to traditional discriminative models?
✓ Correct answer: D. The ability to create new content that resembles the training data
Generative AI models learn the underlying data distribution and can create new content that resembles the training data, while discriminative models focus only on making predictions or classifications based on input features.
Q18A project team is developing a system to categorize customer support tickets automatically. Which type of neural network architecture would be most appropriate for this text classification task?
✓ Correct answer: C. Transformer-based models
Transformers are particularly well-suited for text classification tasks because they can capture long-range dependencies and contextual information in text through their self-attention mechanism, leading to state-of-the-art performance in NLP tasks like ticket categorization.
Q19In a heuristic search algorithm, what is the primary function of the heuristic?
✓ Correct answer: B. To estimate how close a given state is to the goal state
A heuristic provides an estimated cost or distance to the goal state, helping the search algorithm prioritize more promising paths and improve efficiency by avoiding exhaustive exploration of all possible paths.
Q20What distinguishes k-means from hierarchical clustering algorithms?
✓ Correct answer: A. K-means requires the number of clusters to be specified in advance
K-means requires the number of clusters (k) to be specified in advance, while hierarchical clustering builds a tree of clusters without requiring a pre-specified number, allowing users to choose the appropriate number of clusters after examining the cluster hierarchy.
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